Social Ant based Sensitive Item Hiding with Optimal Side Effects for Data Publishing

Selvan, P. Tamil; Veni, S.

doi:10.17485/ijst/2016/v9i2/81159

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Doan et al [25] enhanced the method in [24] to minimize the side effect in terms of lost non-sensitive rules. Ant colony-based solutions to conceal the frequent itemsets obtained improvement in the performance in terms of side effects [26]. Genetic encoding scheme proposed in [27] utilizes objective function to estimate the effect on non-sensitive rules and provides recursive computation to reduce the side effects.…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving association rule hiding using border based approach

Suma

Shobha

2021

IJEECS

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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